小柯机器人

美国Chan Zuckerberg Biohub研究所Loic A. Roye等研究人员合作发现，自监督的深度学习可编码蛋白质亚细胞定位的高分辨率特征。2022年7月25日，《自然—方法学》杂志在线发表了这项成果。

研究人员提出了cytoself，一种用于完全自我监督的蛋白质定位分析和聚类的深度学习方法。cytoself利用了一种自我监督的训练方案，不需要预先存在的知识、类别或注释。研究人员在OpenCell数据库的1311个内源性标记的蛋白质图像上训练cytoself，发现了一个高度分辨率的蛋白质定位图集，并再现了细胞组织的主要尺度，从粗略的类别，如核和细胞质，到单个蛋白质复合体的微妙定位特征。

研究人员定量验证了cytoself将蛋白质聚集到细胞器和蛋白质复合体中的能力，并表明cytoself优于以前的自我监督方法。此外，为了更好地理解这个模型的内部运作，研究人员剖析了这些聚类所来自的突发特征，在荧光图像的背景下解释它们，并分析了这个方法每个组成部分的性能贡献。

据介绍，解释蛋白质定位的多样性和复杂性对于充分理解细胞结构至关重要。

附：英文原文

Title: Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Author: Kobayashi, Hirofumi, Cheveralls, Keith C., Leonetti, Manuel D., Royer, Loic A.

Issue&Volume: 2022-07-25

Abstract: Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.

DOI: 10.1038/s41592-022-01541-z

Source: https://www.nature.com/articles/s41592-022-01541-z

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex